Youping Zhao

Applying Radio Environment Maps to Cognitive Wireless Regional Area Networks

The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks refarming the TV broadcast bands. According to US FCCs recent public notice, WRAN products are scheduled to be available for the market by February, 2009. This paper first presents a brief review of the IEEE 802.22 WRAN standardization, and then introduces the radio environment map (REM) as an innovative cost-efficient approach to developing and managing WRAN systems. REMs can provide powerful infrastructure support to the functionality of the WRAN cognitive engine (CE). The data model of the REM is presented together with an extensive discussion on how to exploit the REM for a variety of applications in WRAN systems, focusing on the REM-enabled case- and knowledge-based learning algorithms (REM-CKL) for WRAN CEs. Furthermore, REM-based radio scenario-driven testing (REM-SDT) is also presented as a viable approach to evaluating the performance of CEs. Future research topics are discussed in the final section.

A Survey of Artificial Intelligence for Cognitive Radios

Cognitive radio (CR) is an enabling technology for numerous new capabilities such as dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this diverse set of applications, CR researchers leverage a variety of artificial intelligence (AI) techniques. To help researchers better understand the practical implications of AI to their CR designs, this paper reviews several CR implementations that used the following AI techniques: artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). Factors that influence the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed. To provide readers with a more concrete understanding, these factors are illustrated in an extended discussion of two CR designs.

Performance Evaluation of Cognitive Radios: Metrics, Utility Functions, and Methodology

Performance evaluation of cognitive radio (CR) networks is an important problem but has received relatively limited attention from the CR community. Unlike traditional radios, a cognitive radio may change its objectives as radio scenarios vary. Because of the dynamic pairing of objectives and contexts, it is imperative for cognitive radio network designers to have a firm understanding of the interrelationships among goals, performance metrics, utility functions, link/network performance, and operating environments. In this paper, we first overview various performance metrics at the node, network, and application levels. From a game-theoretic viewpoint, we then show that the performance evaluation of cognitive radio networks exhibits the interdependent nature of actions, goals, decisions, observations, and context. We discuss the interrelationships among metrics, utility functions, cognitive engine algorithms, and achieved performance, as well as various testing scenarios. We propose the radio environment map-based scenario-driven testing (REM-SDT) for thorough performance evaluation of cognitive radios. An IEEE 802.22 WRAN cognitive engine testbed is presented to provide further insights into this important problem area.

Network Support: The Radio Environment Map

This chapter discusses the strategy of exploiting network support in cognitive radio (CR) systems architectures introducing the radio environment map (REM) as an innovative vehicle of providing network support to CRs. As a systematic top-down approach to providing network support to CRs, the radio environment map is proposed as an integrated database consisting of multi domain information such as geographical features, available services, spectral regulations, locations and activities of radios, policies of the user and/or service provider, and past experience. An radio environment map (REM) can be exploited by a CE to enhance or achieve most of cognitive functionalities such as SA, reasoning, learning, planning, and decision support. Leveraging both internal and external network support through global and local REMs presents a sensible approach to implementing CRs in a reliable, flexible, and cost effective way. Network support can dramatically relax the requirements on a CR device as well as improve the performance of the whole CR network. Considering the dynamic nature of spectral regulation and operation policy, the REM-based CR is flexible and future proof in the sense that it allows regulators or service providers to modify or change their rules or policies simply by updating REMs accordingly.

Chapter 11 – Network Support: The Radio Environment Map

Publisher Summary This chapter discusses the motivation and the important role of network support in cognitive radios. It introduces the radio environment map (REM) and explains how the REM can provide network support to cognitive radios for various applications. The chapter addresses the important role of network support in developing cognitive radios for various application scenarios, including infrastructure-based as well as infrastructure-less ad hoc networks. As a vehicle to providing network support to cognitive radios, the REM is proposed to be an integrated database that consists of multi-domain information, such as geographical features, available services, spectral regulations, locations and activities of radios, policies of the user and/or service provider, and past experience. A cognitive engine to enhance or achieve most cognitive functionalities, such as reasoning, learning, planning, and decision support, can exploit REM. Leveraging both internal and external network support through global and local REMs presents a sensible approach to implement cognitive radios in a reliable, flexible, and cost-effective way. The REM presents a smooth evolutionary path from the legacy radio to the cognitive radio. The REM can be viewed as a natural, but major evolution of radio resource management used in todays commercial wireless networks.

Development of Radio Environment Map Enabled Case- and Knowledge-Based Learning Algorithms for IEEE 802.22 WRAN Cognitive Engines

The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks in unlicensed television broadcast bands. With the intent of efficiently occupying under-utilized spectrum, the network must be cognizant of spectrum available for secondary use and vacate channels as primary users are present. According to FCCs recent public notice, WRAN products are anticipated to be available for the market by February, 2009. This paper first presents a generic architecture for WRAN cognitive engines (CE), and details the design of a CE leveraging the radio environment map database and case-and knowledge-based learning algorithms (REM-CKL). Furthermore, the performance of REM-CKL CE has been evaluated under various radio scenarios and compared to search-based optimizers, including a genetic algorithm (GA). The simulated results show that the WRAN CE can make significantly faster adaptations and achieve near-optimal utility by synergistically leveraging REM-CKL and a local search (LS). Insights into REM-CKL, GA, and LS CE have been gained through the WRAN CE testbed development and preliminary testing.

Overhead Analysis for Radio Environment Mapenabled Cognitive Radio Networks

This paper presents a novel and general approach to cognitive radio (CR) networking based on the Radio Environment Map (REM). REM is envisioned to be an integrated database that consists of comprehensive multi-domain information for CR, such as geographical features, available services, spectral regulations, locations and activities of radio devices, policies, and past experiences. Disseminating and sharing REM information offers a proper vehicle of CR network support, which can be exploited by cognitive engine (CE) for most cognitive functionalities such as situation awareness, reasoning, learning, planning and decision support. Tradeoffs have to be made between the performance gain and the cost of overhead. This paper focuses on analyzing the overhead associated with REM dissemination under various scenarios. With analytical models and network simulations, it is shown that the overhead of REM dissemination can be significantly reduced by extending the optimized link state routing protocol (OLSR). Application-specific ad hoc methods have also been proposed and can be employed to further reduce the overhead. Simulations are presented, comparing the overhead of REM dissemination for different network size, topology, node density and mobility. Preliminary results show that the speed of wireless nodes has little impact to the load of overhead if the REM dissemination rate is fixed. The size of REM information element is estimated for the emerging cognitive wireless regional area networks (IEEE 802.22 WRAN).

The application of distributed spectrum sensing and available resource maps to cognitive radio systems

In order for cognitive radio systems to fulfill their potential of enabling more efficient spectrum utilization by means of opportunistic spectrum use, significant advances must be made in the areas of spectrum sensing and ldquocognitiverdquo spectrum access. In this paper, we discuss two research efforts relevant to these areas; namely the development of distributed (cyclic feature-based) spectrum sensing algorithms and of available resource maps-based cognitive radio systems. It is shown that distributed spectrum sensing is a practical and efficient approach to increase the probability of signal detection and correct modulation classification and/or to reduce sensitivity requirements of individual radios. Additionally, numerical results are presented that show significant reduction of harmful interference and greater spectrum utilization efficiency of available resource maps-based cognitive radio systems.

Experimental study of utility function selection for video over IEEE 802.22 wireless regional area networks

Cognitive Radio (CR) is a new wireless communications and networking paradigm that is enabled by the Software Defined Radio (SDR) technology and a recent change in spectrum regulation policy. As the first commercial application of CR technology, IEEE 802.22 wireless regional area networks (WRAN) aim to offer broadband wireless access by efficiently utilizing “white spaces” in the broadcast TV bands. In this paper, we evaluate the performance of an IEEE 802.22 WRAN base station (BS) cognitive engine (CE) testbed developed at Wireless@Virginia Tech on supporting video applications. We investigate the important problem of utility function selection and its impact on the received video quality. Through testbed experiments, we find that a video-specific utility function achieves significant improvements on received video quality over a general purpose utility function, indicating the efficacy of cross-layer design and more importantly, the need for adopting dynamic situation- and application-aware utility functions at the CE, rather than a predefined static one.

Utility function selection for streaming videos with a cognitive engine testbed

Cognitive Radio (CR) is a new wireless communication and networking paradigm that is enabled by the Software Defined Radio (SDR) technology and the recent change in spectrum regulation policy. As the first commercial application of CR technology, IEEE 802.22 wireless regional area networks (WRAN) aim to offer broadband wireless access by efficiently utilizing the unoccupied TV channels. In this paper, we investigate the problem of utility function selection and its impact on streaming video quality through an IEEE 802.22 WRAN base station (BS) cognitive engine (CE) testbed developed at Wireless@Virginia Tech. We find that significant improvement on received video quality can be achieved when CE adopts a dynamic, content-aware, video-specific utility function rather than a static, predefined, general purpose utility function. This work indicates the importance of video distortion modeling and cross-layer design, and the need for employing dynamic content-aware utility functions at the CE for cognitive streaming video communication networks.